JP6190841B2 - Diagnostic equipment for electric motors - Google Patents

Description

  The present invention relates to a motor diagnosis apparatus that is used in a control center such as a closed switchboard and that diagnoses the presence or absence of an abnormality in an induction motor.

  Conventionally, by measuring the load current of the induction motor and performing frequency analysis, paying attention to the sidebands that occur on both sides of the operating frequency, the short-period vertical waveform disturbance and the long-period vertical waveform An equipment abnormality diagnosis method for diagnosing an abnormality of an induction motor and a device driven by the induction motor based on a state of swell, which is a vibration of the motor, has been proposed. (For example, Patent Document 1)

Japanese Patent No. 4782218

  In the conventional equipment abnormality diagnosis method, when the load torque fluctuation of the induction motor occurs, the spectrum intensity on both sides near the power supply frequency (operating frequency) increases, and the sidebands occur in peaks on both sides of the power supply frequency. There is a problem that it is difficult to detect the sideband wave because it is greater than the vibration intensity of the wave.

  The present invention has been made to solve the above-described problems. Even in an electric motor whose load torque fluctuates, the abnormality of the electric motor can be detected by detecting sideband waves generated in peaks on both sides in the vicinity of the power supply frequency. An object of the present invention is to provide an electric motor diagnosis device capable of diagnosing the presence or absence of the motor.

An electric motor diagnosis apparatus according to the present invention includes a current input unit that detects and inputs an electric current of an electric motor , calculates a variation value based on a standard deviation or a Mahalanobis distance, and obtains a current power spectrum. An FFT analysis section determining section for determining a stable state when a current variation value from the current input section exists in a current section that is equal to or less than a threshold value selected so that the sideband of the FFT can be detected, and the FFT analysis section An FFT analysis unit that analyzes the power spectrum of the current determined to be in a stable state by the determination unit, a peak detection calculation unit that detects a peak portion of the power spectrum obtained by the FFT analysis unit, and an analysis by the FFT analysis unit An averaging calculation unit that averages a plurality of times of the power spectrum obtained, and a side band of the power spectrum averaged by the averaging calculation unit A sideband extractor for extracting, and is characterized in that it comprises an alarm output unit for performing an alarm output when the sideband is extracted signal strength equal to or greater than a set value in the sideband extractor.

According to the present invention, the current input unit that detects and inputs the electric current of the electric motor , the variation value of the current variation data obtained in advance is calculated based on the standard deviation or the Mahalanobis distance, and the sideband of the power spectrum of the current is calculated. When there is a variation value of the current from the current input section in the current section below the threshold value selected for detection, the FFT analysis section determination section for determining the stable state and the FFT analysis section determination section An FFT analysis unit that analyzes the power spectrum of the current determined to be in a state, a peak detection calculation unit that detects a peak portion of the power spectrum obtained by the FFT analysis unit, and a power spectrum that is analyzed by the FFT analysis unit An averaging operation unit that averages a plurality of times and a sideband that extracts sidebands of the power spectrum averaged by the averaging operation unit Since an extraction unit and an alarm output unit that outputs an alarm when a sideband wave having a signal strength equal to or higher than a set value is extracted by the sideband extraction unit, the FFT analysis unit outputs a power spectrum when the current is stable. By performing the analysis, it is possible to reliably detect peak portions occurring on both sides of the power supply frequency. In addition, by averaging the power spectrum multiple times in the averaging calculation unit, the signal intensity at the peak location mixed into the power spectrum due to noise etc. is reduced, and the sideband wave is extracted more reliably by the sideband wave extraction unit Thus, even in an electric motor whose load torque fluctuates, it is possible to obtain an electric motor diagnosis device capable of diagnosing the presence or absence of an electric motor abnormality by detecting sideband waves that occur in a peak shape on both sides near the power supply frequency. There is an effect that can.

It is a schematic block diagram which shows the installation condition of the diagnostic apparatus of the electric motor in Embodiment 1 of this invention. It is a block diagram which shows the structure of the logic operation part of the diagnostic apparatus of the electric motor in Embodiment 1 of this invention. It is explanatory drawing explaining the frequency analysis result in case the load variation of the motor of the motor diagnostic apparatus in Embodiment 1 of this invention is large. It is explanatory drawing explaining the conversion of the frequency axis of the diagnostic apparatus of the electric motor in Embodiment 1 of this invention. It is a flowchart explaining operation | movement of the diagnostic apparatus of the electric motor in Embodiment 1 of this invention. It is a schematic block diagram which shows the installation condition of the diagnostic apparatus of the electric motor in Embodiment 2 of this invention. It is a block diagram which shows the structure of the logic calculating part of the diagnostic apparatus of the electric motor in Embodiment 2 of this invention. It is explanatory drawing explaining the trend analysis of the diagnostic apparatus of the electric motor in Embodiment 2 of this invention. It is explanatory drawing explaining the setting of the threshold value of the diagnostic apparatus of the electric motor in Embodiment 1 of this invention.

Hereinafter, embodiments of the present invention will be described. In the drawings, the same or corresponding parts will be described with the same reference numerals.
Embodiment 1 FIG.
FIG. 1 is a schematic configuration diagram showing an installation state of a motor diagnostic device according to Embodiment 1 of the present invention, and FIG. 2 is a block diagram showing a configuration of a logical operation unit of the motor diagnostic device according to Embodiment 1 of the present invention. FIG. 3 is an explanatory diagram for explaining the frequency analysis result when the load variation of the electric motor of the electric motor diagnosis apparatus according to Embodiment 1 of the present invention is large, and FIG. 4 shows the frequency of the electric motor diagnosis apparatus according to Embodiment 1 of the present invention. FIG. 5 is a flowchart for explaining the operation of the motor diagnostic apparatus according to Embodiment 1 of the present invention, and FIG. 9 is a threshold for the motor diagnostic apparatus according to Embodiment 1 of the present invention. It is explanatory drawing explaining the setting of a value.

  In FIG. 1, the main circuit 1 drawn from the power system includes a circuit breaker 2, an electromagnetic contactor 3, and a current detector such as an instrument transformer that detects a one-phase load current of the three-phase main circuit 1. A vessel 4 is provided. A motor 5 such as a three-phase induction motor as a load is connected to the main circuit 1, and mechanical equipment 6 is driven to operate by the motor 5. The motor diagnosis device 7 is input from the rating information input unit 8 and the rating information input unit 8 in which the power frequency, the rated output of the motor 5, the rated voltage, the rated current, the number of poles, the rated rotational speed, etc. are input in advance. A rating information storage unit 9 for storing the rating information is provided. The rating information is information that can be easily obtained by looking at the catalog of the manufacturer of the motor 5 or the nameplate attached to the motor 5. In addition, when there are a plurality of motors 5 to be diagnosed, rating information of all the motors 5 to be diagnosed is input in advance, but in the following description, one motor 5 will be described. In addition, the motor diagnosis device 7 diagnoses whether there is an abnormality in the motor 5 by using the current input unit 10 for inputting the current detected by the current detector 4 and the current input from the current input unit 10. A logic operation unit 11 and an alarm output unit 21 that outputs an alarm by turning on an alarm or an abnormal lamp when an abnormality is found in the logic operation unit 11 are provided.

  The configuration of the logical operation unit 11 will be described with reference to FIG. The logic operation unit 11 uses the current fluctuation calculation unit 12 to obtain the current fluctuation input from the current input unit 10 and the result obtained by the current fluctuation calculation unit 12 to extract a stable current section. An FFT analysis section determination unit 13 that determines a power spectrum analysis section, an FFT analysis section 14 that performs power spectrum analysis using the current of the section determined by the FFT analysis section determination section 13, and an analysis by the FFT analysis section 14 A peak detection calculation unit 15 for detecting a peak location included in the power spectrum, a rotation frequency band determination unit 16 for obtaining a peak location due to the rotation frequency from the peak location detected by the peak detection calculation unit 15, and a plurality of times Frequency axis conversion calculation unit 17 that matches the frequency of the rotation frequency band of the power spectrum, and the frequency axis converted by frequency axis conversion calculation unit 17 Averaging calculation unit 18 that averages the power spectrum for several times and the power spectrum averaged by averaging calculation unit 18 are used to extract whether there are peak points on both sides of the power frequency other than the rotation frequency band (Hereinafter, this peak portion is referred to as a sideband wave) and a sideband wave determination unit 20 that determines whether the sideband signal intensity is equal to or higher than a set value when the sideband wave is extracted by the sideband wave extraction unit 19. It is constituted by.

The current fluctuation calculation unit 12 calculates a statistical variation in the current value based on the current from the current input unit 10. The calculation of the variation includes techniques such as standard deviation and Mahalanobis distance.
The FFT analysis section determination unit 13 extracts only the current section in which the current value whose variation is equal to or less than the threshold value is stable, from the statistical variation of the current value obtained by the current fluctuation calculation unit 12, and the power spectrum analysis section To decide. In general, when the load torque of the motor 5 fluctuates, the current value varies, and when a power spectrum analysis of a current waveform having a large variation is performed, the signal intensity on both sides near the power supply frequency increases as shown in FIG. As a result, peak points such as sidebands do not appear. In order to prevent this, a threshold value of the FFT analysis section determination unit 13 is provided.

The FFT analysis unit 14 calculates the current power spectrum intensity by performing frequency analysis using the current waveform input in the section determined by the FFT analysis section determination unit 13. By performing the power spectrum analysis with a current waveform in a state where the current value is stable, the power spectrum intensity does not increase on both sides in the vicinity of the power supply frequency, and the peak portion appears surely.
The peak detection calculation unit 15 detects the peak location due to the power supply frequency, the peak location due to the rotation frequency, the peak location due to the sideband, and other peak locations from the analysis result of the current power spectrum intensity. The peak location can be detected by extracting a portion where the steep slope of the result calculated by the first-order, second-order and third-order differential calculations is inverted. By performing the differential calculation up to the third order, it is possible to detect a peak portion having a smaller signal intensity. The peak portion due to the power supply frequency can be easily confirmed because it occurs at the position of the power supply frequency (generally 50 Hz or 60 Hz) stored in the rating information storage unit 9.

  The rotation frequency band determination unit 16 obtains a rotation frequency from the rated rotation number stored in the rating information storage unit 9, and the signal intensity near the position shifted by the rotation frequency on both sides with respect to the power supply frequency is a similar peak point. To extract. Generally, since the motor 5 slips according to the state of the load torque and the rotational speed is deviated, the peak portion due to the rotational frequency also appears deviated accordingly. The rotational frequency band determination unit 16 extracts a peak portion in the frequency band considering this shift and determines it as a rotational frequency band.

  The frequency axis conversion calculation unit 17 is necessary for correctly performing the averaging calculation performed by the averaging calculation unit 18. In general, the position of the sideband generated by the abnormality of the electric motor 5 is closely related to the rotational frequency band, and the frequency band of the sideband is often a multiple of the rotational frequency band. Further, as described above, the rotation frequency band appears with a shift depending on the load torque of the electric motor 5. For this reason, it is necessary to match the frequency axis of the power spectrum analysis results for a plurality of times of averaging by the peak location tracking method. Specifically, as shown in FIG. 4, the frequency of the rotation frequency band is a position away from the power supply frequency by fr, and the sideband frequency is a position away from the power supply frequency by fb, and the electric motor 5 is in an unloaded state. If the frequency in the rotational frequency band is located away from the power supply frequency fr ′, the conversion rate α is α = fr ′ / fr, and the sideband wave position fb ′ at no load is obtained by fb ′ = α · fb. I can do it. In this way, the conversion of the frequency axis of all the peak locations is performed by multiplying the conversion rate α with the rotational frequency band as a reference. In the above description, the case where the frequency axis is adjusted when there is no load has been described. For example, the frequency axis conversion calculation unit 17 adjusts the frequency axis of the power spectrum analysis results for a plurality of times of averaging, such as adjusting the frequency axis when rated load is applied. What is necessary is just to be comprised so that it may match at the time of predetermined | prescribed load.

  The averaging calculation unit 18 averages the power spectrum analysis results for a plurality of times in which the frequency axis is matched by the frequency axis conversion calculation unit 17. The averaging process reduces the base noise to reduce the S of the peak portion. / N ratio can be improved. Specifically, when the power spectrum analysis results for 10 times are averaged, the peak portion due to noise or the like that has occurred only once is reduced to 1/10 of the signal intensity. On the other hand, if the frequency band is a rotational frequency band or sideband, the peak location occurs 10 times, and the frequency is matched by converting the frequency axis using the peak tracking method. It does not change. In the above description, the case where the power spectrum analysis results for 10 times are averaged has been described. However, the present invention is not limited to 10 times, and a plurality of times may be averaged.

The sideband wave extraction unit 19 extracts, as sideband waves, peak portions that are generated at positions shifted by the same frequency on both sides around the power supply frequency from the power spectrum analysis result averaged by the averaging calculation unit 18. For the sideband candidate, the peak position obtained by the peak detection calculation unit 15 is selected as a candidate. When the peak portion occurs only on one side with the power supply frequency as the center, it is determined that it is not a sideband and is not extracted.
The sideband wave determination unit 20 determines whether or not the motor 5 is abnormal from the number of sideband waves extracted by the sideband wave extraction unit 19 and the signal intensity. When it is determined that the electric motor 5 is abnormal, an alarm is output from the alarm output unit 21.

  Next, the operation will be described with reference to FIG. The electric motor diagnosis device 7 is activated at predetermined time intervals to execute the following processing. In step 101, the current of the electric motor 5 detected by the current detector 4 is input by the current input unit 10. In step 102, the fluctuation of the effective value (hereinafter referred to as current value) of the current input from the current input unit 10 is calculated by the current fluctuation calculation unit 12, and the FFT analysis interval determination unit 13 is used by using the calculation result. To determine whether the current is in a stable state. If the determination result shows that the variation in the current value is an unstable state (NO) equal to or greater than a preset threshold value, the process returns to step 101 and is repeated until the current becomes stable. If the current is in a stable state (YES), the process proceeds to step 103. As for the threshold value, for example, field data of a plurality of motors are acquired in advance, and the current variation value (standard deviation) of the data is selected within a small variation value range, and the selected value is set as a threshold. Value. As a specific calculation example, for example, as shown in FIG. 9, the variation value is calculated 50 times, and 0.8, which is the fifth smallest variation value in the rearranged order, is determined as the threshold value. . Instead of the field data in advance, a fixed learning period may be provided in the electric motor 5, and the same calculation may be performed from the current variation value (standard deviation) acquired during the learning period.

  In Step 103, the FFT analysis unit 14 performs frequency analysis between 0 Hz and a frequency 120 Hz that is twice the power supply frequency 60 Hz using the current waveform in the section where the input current value is in a stable state, and the result of the power spectrum analysis To the peak detection calculation unit 15. In step 104, the peak detection calculation unit 15 detects all peak portions included in the power spectrum analysis result. In step 105, the rotation frequency band determination unit 16 extracts a peak position in the rotation frequency band from the detected peak positions and determines a rotation frequency band. In step 106, the frequency axis conversion calculation unit 17 converts the frequency axes of all peak portions so that the detected rotation frequency band becomes the rotation frequency band at the time of no load. In Step 107, the operation from Step 101 to Step 106 is repeated 10 times, and 10 power spectrum analysis results whose frequency axes are converted are collected.

  In step 108, the averaging calculation unit 18 averages the collected ten power spectrum analysis results. In step 109, the sideband wave extraction unit 19 extracts sideband waves by paying attention to the peak portion of the averaged power spectrum analysis result. In step 110, when the sideband wave is not extracted by the sideband wave extraction unit 19 or when the sideband wave is extracted but the signal intensity is smaller than the set value, the sideband wave determination unit 20 detects that the motor 5 is abnormal. Is not generated (NO), and the diagnosis process is terminated. On the other hand, if the signal strength of the sideband extracted by the sideband extraction unit 19 is greater than the set value (YES), a signal is sent to the alarm output unit 21 as an abnormality has occurred in the electric motor 5, and step 111 is performed. Then, an alarm is output from the alarm output unit 21 and the diagnosis process is terminated. As for the set value of the sideband determination unit 20, the signal intensity A of the normal sideband is learned, the standard deviation σ is calculated, and the detected sideband peak value is 99.7%, which is A + 3σ. The range in which data exists is set as the setting value. Alternatively, A + 3σ × α may be obtained by multiplying by a safety coefficient α (for example, 2 or more). Another method for determining the set value is determined from past data at the time of the failure of the same motor. As the number of failure cases increases, an accurate failure location and failure degree can be determined by sidebands. It becomes like this.

  As described above, a peak portion such as a sideband appears surely by analyzing the power spectrum of the current waveform when the current value is stable. Further, by performing the averaging process, noise and the like are reduced, and a more accurate failure diagnosis can be performed.

Embodiment 2. FIG.
FIG. 6 is a schematic configuration diagram showing the installation status of the motor diagnostic device according to Embodiment 2 of the present invention, and FIG. 7 is a block diagram showing the configuration of the logical operation unit of the motor diagnostic device according to Embodiment 2 of the present invention. FIG. 8 is an explanatory diagram for explaining the trend analysis of the electric motor diagnosis apparatus according to the second embodiment of the present invention. In the first embodiment, the case where the abnormality of the electric motor 5 is diagnosed using the averaged power spectrum analysis result has been described. However, in the second embodiment, the averaged power spectrum analysis result is time-series. A case will be described in which trend monitoring is performed after saving the file.

  In FIG. 6, the motor diagnosis apparatus 7 is provided with an FFT information storage unit 22 for storing the averaged power spectrum analysis results in time series. As shown in FIG. An analysis unit 23 is provided.

  Next, the operation of the trend analysis unit 23 will be described. The trend analysis unit 23 pays attention to the sideband wave of a specific frequency of the power spectrum analysis result stored in the FFT information storage unit 22 in time series, and the signal intensity of the sideband wave is time-series as shown in FIG. It is what you want to display. The set values in FIG. 8 are set values used by the sideband determination unit 20. By performing the trend analysis in this manner, for example, if the sideband wave is caused by the wear of the bearing of the electric motor 5, when the signal strength of the sideband wave is small, the degree of wear of the bearing is small and does not become abnormal. However, since the wear of the bearing gradually increases, the signal intensity of the sideband gradually increases in time series as shown in FIG. Therefore, the time when the signal intensity of the sideband reaches the set value and the bearing needs to be replaced can be understood by performing the trend analysis. Since other parts are the same as those in the first embodiment, description thereof is omitted.

  In the present invention, the embodiments can be freely combined within the scope of the invention, and the embodiments can be appropriately modified and omitted.

DESCRIPTION OF SYMBOLS 1 Main circuit, 2 Circuit breaker, 3 Magnetic contactor, 4 Current detector, 5 Electric motor, 6 Mechanical equipment, 7 Motor diagnostic device, 8 Rating information input part, 9 Rating information storage part, 10 Current input part, DESCRIPTION OF SYMBOLS 11 Logic operation part, 12 Current fluctuation calculation part, 13 FFT analysis area determination part, 14 FFT analysis part, 15 Peak detection calculation part, 16 Rotation frequency band determination part, 17 Frequency axis conversion calculation part, 18 Average calculation part, 19 Sideband extraction unit, 20 Sideband determination unit, 21 Alarm output unit, 22 FFT information storage unit, 23 Trend analysis unit

Claims (3)

The current input unit that detects and inputs the current of the motor and the current variation data acquired in advance are calculated based on the standard deviation or Mahalanobis distance, and selected so that sidebands of the current power spectrum can be detected. When there is a variation value of the current from the current input unit in the current section below the threshold value , the FFT analysis section determination section that determines the stable state, and the FFT analysis section determination section that determines the stable state An FFT analysis unit for analyzing a power spectrum of current, a peak detection calculation unit for detecting a peak portion of the power spectrum obtained by the FFT analysis unit, and averaging a plurality of times of the power spectrum analyzed by the FFT analysis unit An averaging calculation unit, a sideband wave extraction unit that extracts a sideband wave of the power spectrum averaged by the averaging calculation unit, and the side Diagnostic apparatus for an electric motor, characterized in that it comprises an alarm output unit for performing an alarm output when the sideband signal intensity equal to or greater than a set value in the wave extractor has been extracted.   The motor diagnosis apparatus according to claim 1, wherein the averaging operation unit converts the frequency axis of the power spectrum for a plurality of times and performs averaging in a state in which the peak portions in the rotational frequency band are combined.   The motor diagnostic apparatus according to claim 1 or 2, wherein information on the power spectrum averaged by the averaging calculation unit is stored and the sideband wave is trend monitored.

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